A Systematic Characterization of LLM Inference on GPUs
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Abstract (arXiv)
This work presents a systematic characterization of Large Language Model (LLM) inference to address fragmented understanding. Through comprehensive experiments, we establish a four-dimensional analytical framework: (1) Two-Phase Heterogeneity Observation; (2) Microarchitectural Root Cause Analysis; (3) System Scaling Principles; and (4) Emerging Paradigm Boundaries. Our investigation progresses systematically from observation to foresight: identifying performance phenomena, revealing hardware causes, validating system behavior, and exploring new paradigms. This study not only consolidates a reliable empirical foundation for existing research but also provides new discoveries and practical optimization guidance for LLM inference.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2025.
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Citing papers per year: 2026: 8.
8 papers.
- 2026TaxBreak: Unmasking the Hidden Costs of LLM Inference Through Overhead Decompositioncited by 2
- 2026Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernelscited by 1
- 2026Selecting Language Models for Social Science: Start Small, Start Open, and Validatecited by 0
- 2026Just Pass Twice: Efficient Token Classification with LLMs for Zero-Shot NERcited by 0
- 2026LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellitescited by 0
- 2026EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Servingcited by 0
- 2026Threshold-Based Exclusive Batching for LLM Inferencecited by 0
- 2026From Caveman to Expert Analyst: Energy Consumption of Variable LLM Taskscited by 0
- 2026From Caveman to Expert Analyst: Energy Consumption of Variable LLM Tasks2 Jul 2026
- 2026Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels2 Jun 2026
- 2026Threshold-Based Exclusive Batching for LLM Inference30 May 2026
- 2026EnergyLens: Interpretable Closed-Form Energy Models for Multimodal LLM Inference Serving11 May 2026
- 2026LLMSpace: Carbon Footprint Modeling for Large Language Model Inference on LEO Satellites7 May 2026
- 2026Just Pass Twice: Efficient Token Classification with LLMs for Zero-Shot NER6 Apr 2026
- 2026TaxBreak: Unmasking the Hidden Costs of LLM Inference Through Overhead Decomposition12 Mar 2026
- 2026Selecting Language Models for Social Science: Start Small, Start Open, and Validate16 Jan 2026